An updated understanding of the natural history of cervical human papillomavirus infection—clinical implications
Bibliographic record
Abstract
Recently, the International Papillomavirus Society convened a working group on cervical human papillomavirus latency, which resulted in an updated understanding of the human papillomavirus natural history. While the previous human papillomavirus natural history model considered human papillomavirus detection to be a result of human papillomavirus acquisition or possibly reinfection, and loss of human papillomavirus detection to be a result of viral clearance, the updated understanding of the human papillomavirus natural history is more nuanced. Thus, human papillomavirus detection may occur as a result of autoinoculation, deposition from a recent sex act, or as a redetection of a previously acquired infection. Similarly, loss of human papillomavirus detection likely reflects immune control rather than complete viral clearance. As it is practically impossible to identify the "true" source of a new human papillomavirus detection or determine why human papillomavirus is no longer detectable, we propose that healthcare providers and researchers use the terminology human papillomavirus detected vs human papillomavirus not detected. Moreover, we describe the updated understanding in a clinical context. Specifically, we discuss the potential implications of the updated understanding regarding clinical counseling in screening, recommendations on cervical screening, and human papillomavirus vaccination. We also suggest key phrases that healthcare providers may use when counseling women attending routine human papillomavirus-based cervical screening.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".